Here we provide more details on the effect of LICE - adding our loss function \(\mathcal {L}_\text{vol\_sum}^\text{sort}\)  to the cross entropy loss in MagNet, DGCN, DiGCN, DiGCN_app, and DiGCN_ib. For a fair
comparison of the methods,
hyperparameters are not tuned; hence the methods may not achieve their optimal performance on the data sets.
For
Cora-ML and CiteSeer [1]}, we discover that their “ground-truth" labels do not give strong cut flow imbalance between classes, as shown in Figures REF  and REF . Hence, as an imbalance-driven method, DIGRAC could not achieve leading performance in classification accuracy, as indicated in the first row of Table REF  and that of Table REF .
Note that we train DIGRAC in a semi-supervised manner, applying cross-entropy loss and triplet loss on all training nodes, i.e., treating all training nodes as seed nodes. Details of the semi-supervised settings are discussed in sec:semisupervised.
We use cross-entropy loss for all the comparing GNNs as the loss function.
